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MODEL SELECTION AND AVERAGING OF HEALTH COSTS IN EPISODE TREATMENT GROUPS

Published online by Cambridge University Press:  21 December 2016

Shujuan Huang ,
Brian Hartman  and
Vytaras Brazauskas
Shujuan Huang
Affiliation:
Data Science Professional Resource Group at Liberty Mutual, Seattle, WA 98154, USA E-Mail: [email protected]
Brian Hartman*
Affiliation:
Department of Statistics, Brigham Young University, Provo, UT 84602, USA
Vytaras Brazauskas
Affiliation:
Department of Mathematical Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA E-Mail: [email protected]
*
E-Mail: [email protected]
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Abstract

Episode Treatment Groups (ETGs) classify related services into medically relevant and distinct units describing an episode of care. Proper model selection for those ETG-based costs is essential to adequately price and manage health insurance risks. The optimal claim cost model (or model probabilities) can vary depending on the disease. We compare four potential models (lognormal, gamma, log-skew-t and Lomax) using four different model selection methods (AIC and BIC weights, Random Forest feature classification and Bayesian model averaging) on 320 ETGs. Using the data from a major health insurer, which consists of more than 33 million observations from 9 million claimants, we compare the various methods on both speed and precision, and also examine the wide range of selected models for the different ETGs. Several case studies are provided for illustration. It is found that Random Forest feature selection is computationally efficient and sufficiently accurate, hence being preferred in this large data set. When feasible (on smaller data sets), Bayesian model averaging is preferred because of the posterior model probabilities.

Keywords

Akaike weightsBayesian model selectionmodel averagingrandom forest
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 47 , Issue 1 , January 2017 , pp. 153 - 167
Copyright
Copyright © Astin Bulletin 2016 

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